High performance evaluation of evolutionary-mined association rules on GPUs

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Alberto Cano and Jose Maria Luna and 
                 Sebastian Ventura",
  title =        "High performance evaluation of evolutionary-mined
                 association rules on GPUs",
  journal =      "The Journal of Supercomputing",
  year =         "2013",
  volume =       "66",
  number =       "3",
  pages =        "1438--1461",
  month =        dec,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Performance
                 evaluation, Association rules, Parallel computing,
  ISSN =         "0920-8542",
  language =     "English",
  URL =          "http://link.springer.com/article/10.1007/s11227-013-0937-4/fulltext.html",
  DOI =          "doi:10.1007/s11227-013-0937-4",
  size =         "24 pages",
  abstract =     "Association rule mining is a well-known data mining
                 task, but it requires much computational time and
                 memory when mining large scale data sets of high
                 dimensionality. This is mainly due to the evaluation
                 process, where the antecedent and consequent in each
                 rule mined are evaluated for each record. This paper
                 presents a novel methodology for evaluating association
                 rules on graphics processing units (GPUs). The
                 evaluation model may be applied to any association rule
                 mining algorithm. The use of GPUs and the compute
                 unified device architecture (CUDA) programming model
                 enables the rules mined to be evaluated in a massively
                 parallel way, thus reducing the computational time
                 required. This proposal takes advantage of concurrent
                 kernels execution and asynchronous data transfers,
                 which improves the efficiency of the model. In an
                 experimental study, we evaluate interpreter performance
                 and compare the execution time of the proposed model
                 with regard to single-threaded, multi-threaded, and
                 graphics processing unit implementation. The results
                 obtained show an interpreter performance above 67
                 billion giga operations per second, and speed-up by a
                 factor of up to 454 over the single-threaded CPU model,
                 when using two NVIDIA 480 GTX GPUs. The evaluation
                 model demonstrates its efficiency and scalability
                 according to the problem complexity, number of
                 instances, rules, and GPU devices.",

Genetic Programming entries for Alberto Cano Rojas Jose Maria Luna Sebastian Ventura